i am implementing a surveillance system and i was looking for algorithm or any resource that could help me with activity Recognition. activity like punch, kick and etc. so when someone kick or punch in a record video the system could recognize that activity.
Human activity/action recognition is one of the hottest topics in ML.
There are challenges like THUMOS, Activity-Net, UCF-101 where this topic is widely studied.
Some of the winners of these challenges have released their codes, you can take a look at them. They have also given how to train our own network with custom dataset and labels. If you don't find the actions that you require in that list, you can build a dataset and train their networks on it.
CUHK
Activity Localisation
Related
I want to have an Auto Speech Recognizer with the trained platform i.e. voice mods.
for eg:-
i have two words very similar in saying so the system must listen to the compplete word and in any dilax and verify it and give the output.
How to do it.
I have searched but i'm completely blank on this point.
Which technology do you want to use? There are different frameworks available out there, e.g. the Dragonfly framework (https://code.google.com/p/dragonfly) or the System.Speech.Recognition namespace for .net projects. For mobile devices you could take a closer look at the speech recognition API offered by Google.
In this point of view, fine tuning with Android speech recognition API is not possible.
you may need to start from the scratch to do this..
if you want to keep using google speech recognition API, then you need to do post processing... this called NLU (Natural Language Understanding) or NLP (Natural Language Processing).
simple concept is whatever STT (speech to text) result came from google API, you need to grouping them into one final output. what so ever your different accent or intonation to be one. or this process has value when it needs some contents to understand and do some action like what is the weather in Seoul?
Going back to your question, fine tuning for differentiating similar pronunciation words needs to have AM (Acoustic Model) and LM (Language Model) which was trained that kinds of words set accordingly. So you need to train the model from the scratch or using exist model with acoustic model adaptation will also works.
for good start point with opensource is HTK or Sphinx. If you have budget to buy, then AT&T's watson is the best tools for speech recognition area so far.
I think you should take a different approach, that is simpler than trying to get Sphinx to work.
Use a phonetic matching algorithm like soundEx to find if the user is more likely to have said one word or the other. I would modify the soundEx algorithm to make it easier to match strings. If your words of different enough it should do a good job.
Here is some code to do it
I'm interesting in finding songs based on attributes (minor key tonality, etc). These are things listed in the details of why Pandora picks songs, but using Pandora, I have to give it songs/artists.
Is there any way to get the Music Genome database (or something similar) so I can search for songs based on attributes (that someone else has already cataloged)
You can use Gracenote's Global Media Database and search with Track-level attributes.
"Gracenote's Media Technology Lab scientists and engineers take things further by utilizing technologies like Machine-Listening and Digital Signal Processing to create deep and detailed track level descriptors such as Mood and Tempo."
I don't think there is any way to access this proprietary data, something I asked them about long ago. It seems to me they want to protect this unique part of their system; after all, they've paid for the man hours to label each song. Even if Pandora releases a developer API, which they've hinted at, I doubt it will provide access to the Music Genome information.
Give Echo Nest a shot!
To add to above answers, Pandora's statement (as viewed using the above link in combination with the Internet Archive) was:
"A number of folks also asked about the prospect for an open API, to allow individual developers to start building on the platform. We're not there yet, but it's certainly food for thought."
Given that this was seven years ago, I think their decision is pretty clear.
I am a beginner Silverlight programmer preparing for the interview in medical research company. Job sounds damn interesting and I would like to get there.
To show my skills and interest, I want to write a program related to the topic.
What would you suggest?
First ideas: simple statistical analysis of input data, image collections (for example, find HD DNA image and put it in Silverlight Deep Zoom), lab inventory program..
Have a look at http://research.microsoft.com/en-us/projects/bio/default.aspx the Microsoft Biology Foundation, part of Microsoft Research. Its code is OpenSource (sic) and you will find many applications there. The apps cover most of the basics, sequences, etc. and have some nice display tools.
Collection/Maintenance/Retrieval of data is very important for any organization. Try this tutorial:
Silverlight tutorial: Creating a data centric web app with DataGrid, LINQ, and WCF Web Service
You placed a "bioinformatics" tag on your question. Many bioinformatics companies consider Perl programming to be quite important.
I suggest that you perform a search on "bioinformatics Perl" and take a look at books and sites that are retrieved. Perhaps you could park yourself at a local bookstore and peruse some of those titles. Free Perl interpreters are available.
You do have a basic understanding of genetics, yes? Be very familiar with some of the terminology, so you won't have to stare off into space while you pick from genotype/phenotype or mRNA/RNA/DNA or recall what a codon is.
It wouldn't hurt to nose around PubMed and get a basic understanding of what genomes are out there and what statistical tests can be performed on them.
I like your statistics idea. Perhaps you could write a program that tells you whether to accept or reject a null hypothesis based on numbers you read in from a file. Or perhaps you could figure out how to use the statistics portion of Entrez Gene in PubMed.
Best wishes for the interview.
I need help to chose a project to work on for my master graduation, The project must involve Ai / Machine learning or Business intelegence.. but if there is any other suggestion out of these topics it is Ok, please help me.
One of the most rapid growing areas in AI today is Computer Vision. There are many practical needs where the results of your Master Thesis can be helpful. You can try research something like Emotion Detection, Eye-Tracking, etc.
An appropriate work for a MS in CS in any good University can highlight the current status of research on this field, compare different approaches and algorithms. As a practical part, it makes also a lot of fun when your program recognizes your mood properly :)
Netflix
If you want to work more on non trivial datasets (not google size, but not trivial either and with real application), with an objective measure of success, why not working on the netflix challenge (the first one) ? You can get all the data for free, you have many papers on it, as well as pretty good way to compare your results vs other peoples (since everyone used exactly the same dataset, and it was not so easy to "cheat", contrary to what happens quite often in the academic literature). While not trivial in size, you can work on it with only one computer (assuming it is recent enough), and depending on the type of algorithms you are using, you can implement them in a language which is not C/C++, at least for prototyping (for example, I could get decent results doing things entirely in python).
Bonus point, it passes the "family" test: easy to tell your parents what you are working on, which is always a pain in my experience :)
Music-related tasks
A bit more original: something that is both cool, not trivial but not too complicated in data handling is anything around music, like music genre recognition (classical / electronic / jazz / etc...). You would need to know about signal processing as well, though - I would not advise it if you cannot get easy access to professors who know about the topic.
I can use the same answer I used on a previous, similar question:
Russ Greiner has a great list of project topics for his machine learning course, so that's a great place to start.
Both GAs and ANNs are learners/classifiers. So I ask you the question, what is an interesting "thing" to learn? Maybe it's:
Detecting cancer
Predicting the outcome between two sports teams
Filtering spam
Detecting faces
Reading text (OCR)
Playing a game
The sky is the limit, really!
Since it has a business tie in - given some input set determine probable business fraud from the input (something the SEC seems challenged in doing). We now have several examples (Madoff and others). Or a system to estimate investment risk (there are lots of such systems apparently but were any accurate in the case of Lehman for example).
A starting point might be the Chen book Genetic Algorithms and Genetic Programming in Computational Finance.
Here's an AAAI writeup of an award to the National Association of Securities Dealers for a system thatmonitors NASDAQ insider trading.
Many great answers posted already, but I wanted to add my 2 cents.There is one hot topic in which big companies all around are investing lots of resources into, and is still a very challenging topic with lots of potential: Automated detection of fake news.
This is even more relevant nowadays where most of us are connecting though social media and there's a huge crisis looming over.
Fake news, content removal, source reliability... The problem is huge and very exciting. It is as I said challenging as it can be seen from many perspectives (from analising images to detect fakes using adversarial netwotks to detecting fake written news based on text content (NLP) or using graph theory to find sources) and the possbilities for a research proyect are endless.
I suggest you read some general articles (e.g this or this) or have a look at research articles from the last couple of years (a quick google seach will throw you a lot of related stuff).
I wish I had the opportunity of starting over a project based on this topic. I think it's going to be of the upmost relevance in the next few years.
I was wondering if you creative minds out there could think of some situations or applications in the web environment where Neural Networks would be suitable or an interesting spin.
Edit: Some great ideas here. I was thinking more web centric. Maybe bot detectors or AI in games.
To name a few:
Any type of recommendation system (whether it's movies, books, or targeted advertisement)
Systems where you want to adapt behaviour to user preferences (spam detection, for example)
Recognition tasks (intrusion detection)
Computer Vision oriented tasks (image classification for search engines and indexers, specific objects detection)
Natural Language Processing tasks (document/article classification, again search engines and the like)
The game located at 20q.net is one of my favorite web-based neural networks. You could adapt this idea to create a learning system that knows how to play a simple game and slowly learns how to beat humans at it. As it plays human opponents, it records data on game situations, the actions taken, and whether or not the NN won the game. Every time it plays, win or lose, it gets a little better. (Note: don't try this with too simple of a game like checkers, an overly simple game can have every possible game/combination of moves pre-computed which defeats the purpose of using the NN).
Any sort of classification system based on multiple criteria might be worth looking at. I have heard of some company developing a NN that looks at employee records and determines which ones are the least satisfied or the most likely to quit.
Neural networks are also good for doing certain types of language processing, including OCR or converting text to speech. Try creating a system that can decipher capchas, either from the graphical representation or the audio representation.
If you screen scrap or accept other sites item sales info for price comparison, NN can be used to flag possible errors in the item description for a human to then eyeball.
Often, as one example, computer hardware descriptions are wrong in what capacity, speed, features that are portrayed. Your NN will learn that generally a Video card should not contain a "Raid 10" string. If there is a trend to add Raid to GPUs then your NN will learn this over time by the eyeball-er accepting an advert to teach the NN this is now a new class of hardware.
This hardware example can be extended to other industries.
Web advertising based on consumer choice prediction
Forecasting of user's Web browsing direction in micro-scale and very short term (current session). This idea is quite similar, a generalisation, to the first one. A user browsing Web could be proposed with suggestions with other potentially interesting websites. The suggestions could be relevance-ranked according to prediction calculated in real-time during user's activity. For instance, a list of proposed links or categories or tags could be displayed in form of a cloud and font size indicates rank score. Each and every click a user makes is an input to the forecasting system, so the forecast is being constantly refined to provide user with as much accurate suggestions, in terms of match against user's interest, as possible.
Ignoring the "Common web problems" angle request but rather "interesting spin" view.
One of the many ways that a NN can be viewed/configured, is as a giant self adjusting, multi-input, multi-output kind of case flow control.
So when you want to offer match ups that are fuzzy, (not to be confused directly with fuzzy logic per se, which is another area of maths/computing) NN may offer a usable alternative.
So to save energy, you offer a lift club site, one-offs or regular trips. People enter where they are, where they want to go and at what time. Sort by city and display in browse control.
Using a NN you could, over time, offer transport owners to transport seekers by watching what owners and seekers link up. As a owner may not live in the same suburb that a seeker resides. The NN learns over time what variances in owners, seekers physical location difference appear to be acceptable. So it can then expand its search area when offering a seekers potential owners.
An idea.
Search! Recognize! Classify! Basically everything search engines do nowadays could benefit from a dose of neural networks and fuzzy logic. This applies in particular to multimedia content (e.g. content-indexing images and videos) since that's where current search technologies are lagging behind.
One thing that always amazes me is that we still don't have any pseudo-intelligent firewalling technology. Something that says "hey his range of urls is making too much requests when they are not supposed to", blocks them, and sends a report to an administrator. That could be done with a neural network.
On the nasty part of things, some virus makers could find lucrative uses to neural networks. Adaptative trojans that "recognized" credit card numbers on a hard drive (instead of looking for certain cookies) or that "learn" how to mask themselves from detectors automatically.
I've been having fun trying to implement a bot based on a neural net for the Diplomacy board game, interacting via DAIDE protocols. It turns out to be extremely tricky, so I've turned to XCS to simplify the problem.
Suppose EBay used neural nets to predict how likely a particular item was to sell; predict what the best day to list items of that type would be, suggest a starting price or "buy it now price"; or grade your description based on how likely it was to attract buyers? All of those could be useful features, if they worked well enough.
Neural net applications are great for representing discrete choices and the whole behavior of how an individual acts (or how groups of individuals act) when mucking around on the web.
Take news reading for instance:
Back in the olden days, you picked up usually one newspaper (a choice), picked a section (a choice), scanned a page and chose an article (a choice), and read the basics or the entire article (another choice).
Now you choose which news site to visit and continue as above, but now you can drop one paper, pick up another, click on ads, change sections, and keep going with few limits.
The whole use of the web and the choices people make based on their demographics, interests, experience, politics, time of day, location, etc. is a very rich area for NN application. This is especially relevant to news organizations, web page design, ad revenue, and may even be an under explored area.
Of course, it's very hard to predict what one person will do, but put 10,000 of them that are the same age, income, gender, time of day, etc. together and you might be able to predict behavior that will lead to better designs. Imagine a newspaper (or even a game) that could be scaled to people's needs based on demographics. An ad man's dream !
How about connecting users to the closest DNS, and making sure there are as few bounces as possible between the request and the destination?
Friend recommendation in social apps (Linkedin,facebook,etc)